Thresholds of sea‐level rise rate and sea‐level rise acceleration rate in a vulnerable coastal wetland

Abstract

Feedbacks among inundation, sediment trapping, and vegetation productivity help maintain coastal wetlands facing sea‐level rise (SLR). However, when the SLR rate exceeds a threshold, coastal wetlands can collapse. Understanding the threshold helps address key challenges in ecology—nonlinear response of ecosystems to environmental change, promotes communication between ecologists and resource managers, and facilitates decision‐making in climate change policies. We studied the threshold of SLR rate and developed a new threshold of SLR acceleration rate on sustainability of coastal wetlands as SLR is likely to accelerate due to enhanced anthropogenic forces. Deriving these two thresholds depends on the temporal scale, the interaction of SLR with other environmental factors, and landscape metrics, which have not been fully accounted for before this study. We chose a representative marine‐dominated estuary in the northern Gulf of Mexico, Grand Bay in Mississippi, to test the concept of SLR thresholds. We developed a mechanistic model to simulate wetland change and then derived the SLR thresholds for Grand Bay. The model results show that the threshold of SLR rate in Grand Bay is 11.9 mm/year for 2050, and it drops to 8.4 mm/year for 2100 using total wetland area as a landscape metric. The corresponding SLR acceleration rate thresholds are 3.02 × 10−4 m/year2 and 9.62 × 10−5 m/year2 for 2050 and 2100, respectively. The newly developed SLR acceleration rate threshold can help quantify the temporal lag before the rapid decline in wetland area becomes evident after the SLR rate threshold is exceeded, and cumulative SLR a wetland can adapt to under the SLR acceleration scenarios. Based on the thresholds, SLR that will adversely impact the coastal wetlands in Grand Bay by 2100 will fall within the likely range of SLR under a high warming scenario (RCP8.5), highlighting the need to avoid RCP8.5 to preserve these marshes.

Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities

“…Research to date has focused on identifying synergisms among stressors (Campbell and Fourqurean, 2014;Lefcheck et al, 2017;Moftakhari et al, 2017;Noto and Shurin, 2017), but antagonisms and other feedbacks may be just as common (Brown et al, 2013;Conlisk et al, 2013;Maxwell et al, 2015;Crotty et al, 2017), and are seldom investigated, as is also true for thresholds and tipping points in coastal ecosystem stability and vulnerability (Connell et al, 2017;O'Meara et al, 2017;Wu et al, 2017). This precludes complete understanding of their complex responses, which may be greater than additive responses alone (Crotty et al, 2017), their adequate management, or restoration regimes (Maxwell et al, 2015;Unsworth et al, 2015).…”

Modeling long‐term salt marsh response to sea level rise in the sediment‐deficient Plum Island Estuary, MA

“…These models tend to be developed to explore general behavior or to make predictions for a single location, and rely on relatively complex parameterizations for the flow of water and sediment across the model domain. A second category of models emphasizes vertical accretion on the marsh platform itself (Schile et al 2014;Swanson et al 2014;Alizad et al 2016a;Cadol et al 2016;Wu et al 2017;Thorne et al 2018). These point-based models simplify spatial connectivity, and allow for easier parameterization and application to multiple sites.…”

GIS-Based Scientific Workflows for Automated Spatially Driven Sea Level Rise Modeling

“…A set of landscape metrics were introduced to characterize landscape configuration and fragmentation. Wu et al [39] compared the thresholds of sea-level rise rate with respect to wetland area and landscape metrics such as mean patch size and mesh size. Wu et al stressed that while the use of landscape metrics may lead to different thresholds, it allows for considering landscape characteristics and ecosystem dynamics into SLR studies [39].…”